Decode binary low-density parity-check data with GPU
GPU LDPCDecoder object decodes a binary
low-density parity-check code using a graphics processing unit (GPU).
Note: To use this object, you must install a Parallel Computing Toolbox™ license and have access to an appropriate GPU. For more about GPUs, see GPU Computing in the Parallel Computing Toolbox documentation.
A GPU-based System object™ accepts typical MATLAB® arrays or objects that you create using the gpuArray class as an input to the step method. GPU-based System objects support input signals with double- or single-precision data types. The output signal inherits its datatype from the input signal.
If the input signal is a MATLAB array, then the output signal is also a MATLAB array. In this case, the System object handles data transfer between the CPU and GPU.
If the input signal is a gpuArray, then the output signal is also a gpuArray. In this case, the data remains on the GPU. Therefore, when the object is given a gpuArray, calculations take place entirely on the GPU and no data transfer occurs. Invoking the step method with gpuArray arguments provides increased performance by reducing simulation time. For more information, see Establish Arrays on a GPU in the Parallel Computing Toolbox documentation.
To decode a binary low-density parity-check code:
h = comm.gpu.LDPCDecoder creates a GPU-based
LDPC binary low-density parity-check decoder object,
This object performs LDPC decoding based on the specified parity-check
matrix. The object does not assume any patterns in the parity-check
h = comm.gpu.LDPCDecoder( creates
a GPU-based LDPC decoder object,
h, with each
specified property set to the specified value. You can specify additional
name-value pair arguments in any order as (
h = comm.gpu.LDPCDecoder(PARITY) creates
a GPU-based LDPC decoder object,
h, with the
Specify the parity-check matrix as a binary valued sparse matrix with dimension (N-by-K) by N, where N > K > 0. The last N−K columns in the parity check matrix must be an invertible matrix in GF(2). This property accepts numeric or logical data types. The upper bound for the value of N is (231)-1. The default is the parity-check matrix of the half-rate LDPC code from the DVB-S.2 standard, which is the result of dvbs2ldpc(1/2).
Select output value format
Specify the output value format as one of
Specify the decision method used for decoding as one of
Condition for iteration termination
Specify the condition to stop the decoding iterations as one
Maximum number of decoding iterations
Specify the maximum number of iterations the object uses as
an integer valued numeric scalar. The default is
Output number of iterations performed
Set this property to true to output the actual number of iterations the object performed. The default is false.
Output final parity checks
Set this property to true to output the final parity checks the object calculated. The default is false.
|clone||Create GPU LDPC Decoder object with same property values|
|isLocked||Locked status for input attributes and nontunable properties|
|release||Allow property value and input characteristics changes|
|step||Decode input signal using LDPC decoding scheme|
GPU LDPC Decoder System object uses
the same algorithm as the LDPC Decoder block.
See Decoding Algorithm for details.
Transmit an LDPC-encoded, QPSK-modulated bit stream through an AWGN channel, then demodulate, decode, and count errors.
hEnc = comm.LDPCEncoder; hMod = comm.PSKModulator(4, 'BitInput',true); hChan = comm.AWGNChannel(... 'NoiseMethod','Signal to noise ratio (SNR)','SNR',1); hDemod = comm.PSKDemodulator(4, 'BitOutput',true,... 'DecisionMethod','Approximate log-likelihood ratio', ... 'Variance', 1/10^(hChan.SNR/10)); hDec = comm.gpu.LDPCDecoder; hError = comm.ErrorRate; for counter = 1:10 data = logical(randi([0 1], 32400, 1)); encodedData = step(hEnc, data); modSignal = step(hMod, encodedData); receivedSignal = step(hChan, modSignal); demodSignal = step(hDemod, receivedSignal); receivedBits = step(hDec, demodSignal); errorStats = step(hError, data, receivedBits); end fprintf('Error rate = %1.2f\nNumber of errors = %d\n', ... errorStats(1), errorStats(2))